Collaborative Research: Decoding the Corrosion of Borate Glasses: From Fundamental Science to Quantitative Structure-Property Relationships

点击翻译按钮获取中文摘要

NON-TECHNICAL DESCRIPTION: Chemical durability of glass is a topic of interest today; fundamental understanding is of paramount importance to the glass industry and to the pursuit of overcoming various challenges relevant to the well-being of humanity and the environment, including nuclear waste management and development of novel biomaterials. This project aims at understanding the fundamental science governing corrosion of multicomponent borate glasses, achieved through the unification of experimental studies and artificial intelligence. Successful completion of this project is expected to lay the foundation of new fundamental knowledge to understand and describe composition-structure-property relationships in glass corrosion, and advance new machine learning-based models to promptly and reliably predict the corrosion behavior of borate glasses. The U.S. glass/materials industry is facing a severe shortage of experienced glass engineers/scientists. The project reduces this shortage by training undergraduate and graduate students in glass science and engineering, thus providing a talent pool for the U.S. glass/materials industry, academia, and national laboratories. The education and outreach activities are designed to invoke interest in students and teachers at the middle and high school levels, in addition to the training of undergraduate and graduate science and engineering students. TECHNICAL DETAILS: Our current understanding of glass corrosion is based primarily on empirical data, as there is still no complete consensus on the primary mechanism of glass dissolution that applies across a wide composition space. Therefore, there is an exigent need to develop robust, fundamental understanding of the linkage(s) between chemical composition, atomic/molecular structure, and chemical durability of glasses in order to address crucial and scientifically challenging problems (e.g., designing glasses with desired chemical durability). Accordingly, the project aims at combining the strengths of experimental studies and artificial intelligence to reveal the underlying mechanisms that dictate the dissolution behavior of borate glasses in aqueous environments; and developing a cloud-based quantitative structure-property relationship (QSPR) model – powered by theory-guided machine learning engine – to predict the time-dependent corrosion behavior of oxide glasses. Enabling the materials-by-design approach – which is in alignment with the U.S. Materials Genome Initiative – this project is a pioneering effort, representing a leap forward in designing oxide glasses with controlled chemical durability. Apart from revealing fundamental drivers of glass corrosion and advancing a QSPR model to reliably predict glass corrosion, a significant outcome of the project is the development of a talent pipeline of undergraduate and graduate students well-trained in glass/materials science and machine learning. Further, the project's education plan incorporates a foundational, spiral approach that builds interest at the elementary, middle, and high school level students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.